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A major event just happened in the world of open-source LLMs brought about by the launch of QLoRA and Guanaco. A significant paradigm shift in AI fine-tuning, QLoRA, an open-source method, and Guanaco, the first model perfected with this new technique, have ushered in a groundbreaking era. These developments have democratized the fine-tuning of AI models, significantly reducing the computational resources needed and making it accessible for a wider audience. Traditionally, fine-tuning large AI models demanded substantial computational power and pricey GPU resources, making it inaccessible for many. However, with QLoRA, a 65 billion parameter model can be fine-tuned using a single 48 gigabyte VRAM GPU, a significantly more attainable requirement, as opposed to the previously required over 780 gigabytes of VRAM. This method maintains the high performance of a full 16-bit model in 4-bit quantization, marking an impressive advancement in the field. Guanaco, the first model fine-tuned using QLoRA, showcases its efficacy, achieving 99.3 percent of ChatGPT's performance level in just 24 hours with a single 48 gigabyte GPU. As a testament to the collaborative ethos of the AI community, QLoRA's code, including CUDA kernels for 4-bit training, has been open-sourced under the MIT license, though it's important to note that while QLoRA is open-source, the Guanaco models are not. The paper can be found here: https://arxiv.org/pdf/2305.14314.pdf 0:00 Introducing QLoRA 4:08 Impact on the Open-Source Community 7:40 Overview of How it Works 12:17 Background 18:40 3 Main Techniques of QLoRA 24:44 Comparing With Full-Model Fine-Tuning 27:05 Model Tournament